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Buckeye Corpus
The Buckeye Corpus of conversational speech is a speech corpus created by a team of linguists and psychologists at Ohio State University led by Prof. Mark Pitt. It contains high-quality recordings from 40 speakers in Columbus, Ohio conversing freely with an interviewer. The interviewer's voice is heard only faintly in the background of these recordings. The sessions were conducted as Sociolinguistics interviews, and are essentially monologues. The speech has been orthographically transcribed and phonetically labeled. The audio and text files, together with time-aligned phonetic labels, are stored in a format for use with speech analysis software (Xwaves and Wavesurfer). Software for searching the transcription files is also available at the project web site. The corpus is available to researchers in academia and industry. The project was funded by the National Institute on Deafness and Other Communication Disorders and the Office of Research at Ohio State University.
Magic Quadrant
Magic Quadrant (MQ) is a series of market research reports published by research and advisory firm Gartner that rely on proprietary qualitative data analysis methods to demonstrate market trends, such as direction, maturity, and participants. Their analyses are conducted for several specific technology industries and are updated every 1–2 years: once an updated report has been published, its predecessor is "retired". == Rating == Gartner rates vendors upon two criteria: completeness of vision and ability to execute. Completeness of vision – Reflects the vendor's innovation, and whether the vendor drives or follows the market. Ability to execute – Summarizes factors such as the vendor's financial viability, market responsiveness, product development, sales channels and customer base. The two component scores lead to a vendor position in one of four quadrants: === Leaders === Vendors in the "Leaders" quadrant have the highest composite scores for their completeness of vision and ability to execute. A vendor in the Leaders quadrant has the market share, credibility, and marketing & sales capabilities needed to drive the acceptance of new technologies. These vendors demonstrate a clear understanding of market needs, they are innovators and thought leaders, and they have well-articulated plans that customers and prospects can use when designing their infrastructures and strategies. In addition, they have a presence in the five major geographical regions, consistent financial performance, and broad platform support. === Challengers === Vendors in the "Challengers" quadrant have high scores mainly for their ability to execute. They both participate in the market and execute well enough to be a serious threat to vendors in the "Leaders" quadrant. They have strong products, as well as sufficiently credible market position and resources to sustain continued growth. Financial viability is not an issue for vendors in the "Challengers" quadrant, but they lack the size and influence of vendors in the "Leaders" quadrant due to their relative lack of vision. === Visionaries === Vendors in the "Visionaries" quadrant have high scores mainly for their completeness of vision. They deliver innovative products that address operationally or financially important end-user problems at a broad scale, but have not yet demonstrated the ability to capture market share or maintain sustainable levels of profitability. Visionary vendors are frequently privately held companies and acquisition targets for larger, established companies. The likelihood of acquisition often reduces the risks associated with installing their systems. === Niche Players === Vendors in the "Niche Players" quadrant have relatively low scores for both their ability to execute and their completeness of vision. They are often narrowly focused on specific market or vertical segments. This quadrant often also includes vendors that are adapting their existing products to enter the market under consideration, or larger vendors having difficulty developing and executing on their vision. == Gartner Critical Capabilities == Gartner Critical Capabilities complement Magic Quadrant analysis to offer deeper insight into the products and services offered by multiple vendors by a comparative analysis that scores competing products or services against a set of critical differentiators identified by Gartner. Gartner has periodically ended Magic Quadrant listings for IT Service Management, Web Content Management, and other industries as those markets have fully matured or other factors rendered the analytic framework inapplicable. == Criticism == The Magic Quadrant, and analysts in general, skew the market: according to research, by applying their methodologies to describe a market, they change that marketplace to fit their tools. Another criticism is that open source vendors are not considered sufficiently by analysts like Gartner, as has been published in an online discussion between a VP from Talend and a German Research VP from Gartner. On May 29, 2009 (2009-05-29), software vendor ZL Technologies filed a federal lawsuit against Gartner that challenged the "legitimacy" of Gartner's Magic Quadrant rating system. Gartner filed a motion to dismiss by claiming First Amendment protection since it contends that its MQ reports contain "pure opinion", which legally means opinions that are not based on fact. The court threw out the ZL case because it lacked a specific complaint. The decision was upheld on appeal.
Software intelligence
Software intelligence is insight into the inner workings and structural condition of software assets produced by software designed to analyze database structure, software framework and source code to better understand and control complex software systems in information technology environments. Similarly to business intelligence (BI), software intelligence is produced by a set of software tools and techniques for the mining of data and the software's inner-structure. Results are automatically produced and feed a knowledge base containing technical documentation and blueprints of the innerworking of applications, and make it available to all to be used by business and software stakeholders to make informed decisions, measure the efficiency of software development organizations, communicate about the software health, prevent software catastrophes. == History == Software intelligence has been used by Kirk Paul Lafler, an American engineer, entrepreneur, and consultant, and founder of Software Intelligence Corporation in 1979. At that time, it was mainly related to SAS activities, in which he has been an expert since 1979. In the early 1980s, Victor R. Basili participated in different papers detailing a methodology for collecting valid software engineering data relating to software engineering, evaluation of software development, and variations. In 2004, different software vendors in software analysis started using the terms as part of their product naming and marketing strategy. Then in 2010, Ahmed E. Hassan and Tao Xie defined software intelligence as a "practice offering software practitioners up-to-date and pertinent information to support their daily decision-making processes and Software Intelligence should support decision-making processes throughout the lifetime of a software system". They go on by defining software intelligence as a "strong impact on modern software practice" for the upcoming decades. == Capabilities == Because of the complexity and wide range of components and subjects implied in software, software intelligence is derived from different aspects of software: Software composition is the construction of software application components. Components result from software coding, as well as the integration of the source code from external components: Open source, 3rd party components, or frameworks. Other components can be integrated using application programming interface call to libraries or services. Software architecture refers to the structure and organization of elements of a system, relations, and properties among them. Software flaws designate problems that can cause security, stability, resiliency, and unexpected results. There is no standard definition of software flaws but the most accepted is from The MITRE Corporation where common flaws are cataloged as Common Weakness Enumeration. Software grades assess attributes of the software. Historically, the classification and terminology of attributes have been derived from the ISO 9126-3 and the subsequent ISO 25000:2005 quality model. Software economics refers to the resource evaluation of software in the past, present, or future to make decisions and to govern. == Components == The capabilities of software intelligence platforms include an increasing number of components: Code analyzer to serve as an information basis for other software intelligence components identifying objects created by the programming language, external objects from Open source, third parties objects, frameworks, API, or services Graphical visualization and blueprinting of the inner structure of the software product or application considered including dependencies, from data acquisition (automated and real-time data capture, end-user entries) up to data storage, the different layers within the software, and the coupling between all elements. Navigation capabilities within components and impact analysis features List of flaws, architectural and coding violations, against standardized best practices, cloud blocker preventing migration to a Cloud environment, and rogue data-call entailing the security and integrity of software Grades or scores of the structural and software quality aligned with industry-standard like OMG, CISQ or SEI assessing the reliability, security, efficiency, maintainability, and scalability to cloud or other systems. Metrics quantifying and estimating software economics including work effort, sizing, and technical debt Industry references and benchmarking allowing comparisons between outputs of analysis and industry standards == User aspect == Some considerations must be made in order to successfully integrate the usage of software Intelligence systems in a company. Ultimately the software intelligence system must be accepted and utilized by the users in order for it to add value to the organization. If the system does not add value to the users' mission, they simply don't use it as stated by M. Storey in 2003. At the code level and system representation, software intelligence systems must provide a different level of abstractions: an abstract view for designing, explaining and documenting and a detailed view for understanding and analyzing the software system. At the governance level, the user acceptance for software intelligence covers different areas related to the inner functioning of the system as well as the output of the system. It encompasses these requirements: Comprehensive: missing information may lead to a wrong or inappropriate decision, as well as it is a factor influencing the user acceptance of a system. Accurate: accuracy depends on how the data is collected to ensure fair and indisputable opinion and judgment. Precise: precision is usually judged by comparing several measurements from the same or different sources. Scalable: lack of scalability in the software industry is a critical factor leading to failure. Credible: outputs must be trusted and believed. Deploy-able and usable. == Applications == Software intelligence has many applications in all businesses relating to the software environment, whether it is software for professionals, individuals, or embedded software. Depending on the association and the usage of the components, applications will relate to: Change and modernization: uniform documentation and blueprinting on all inner components, external code integrated, or call to internal or external components of the software Resiliency and security: measuring against industry standards to diagnose structural flaws in an IT environment. Compliance validation regarding security, specific regulations or technical matters. Decisions making and governance: Providing analytics about the software itself or stakeholders involved in the development of the software, e.g. productivity measurement to inform business and IT leaders about progress towards business goals. Assessment and Benchmarking to help business and IT leaders to make informed, fact-based decision about software. == Marketplace == Software intelligence is a high-level discipline and has been gradually growing covering the applications listed above. There are several markets driving the need for it: Application Portfolio Analysis (APA) aiming at improving the enterprise performance. Software Assessment for producing the software KPI and improving quality and productivity. Software security and resiliency measures and validation. Software evolution or legacy modernization, for which blueprinting the software systems are needed nor tools improving and facilitating modifications.
Master data
Master data represents "data about the business entities that provide context for business transactions". The most commonly found categories of master data are parties (individuals and organisations, and their roles, such as customers, suppliers, employees), products, financial structures (such as ledgers and cost centres) and locational concepts. Master data should be distinguished from reference data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. Master data is, by its nature, almost always non-transactional in nature. There exist edge cases where an organization may need to treat certain transactional processes and operations as "master data". This arises, for example, where information about master data entities, such as customers or products, is only contained within transactional data such as orders and receipts and is not housed separately. ISO 8000 is the international standard for data quality and data portability in master data. == Alternative definition == An alternative definition of the term master data is that it represents the business objects that contain the most valuable, agreed upon information shared across an organization. In this sense, it gives context to business activities and transactions, answering questions like who, what, when and how as well as expanding the ability to make sense of these activities through categorizations, groupings and hierarchies. It can cover relatively static reference data, transactional, unstructured, analytical, hierarchical and metadata. What constitutes master data under this definition is therefore not about an essential quality of the data (e.g. it is a business entity that provides context for business transactions), but rather about the context in which the organisation has decided to treat the data. == Externally-defined master data == For most organisations, most or all master data is defined and managed within that organisation. Some master data, however, may be externally defined and managed. This represents the single source of basic business data used across a marketplace, regardless of organisation or location. Thus, it can be used by multiple enterprises within a value chain, facilitating "integration of multiple data sources and literally [putting] everyone in the market on the same page." An example of market master data is the Universal Product Code (UPC) found on consumer products. == Master data management == Curating and managing master data is key to ensuring its quality and thus fitness for purpose. All aspects of an organisation, operational and analytical, are greatly dependent on the quality of an organization's master data. Master Data is therefore the focus of the information technology (IT) discipline of master data management (MDM). Without this discipline in place, organisations commonly encounter difficulties with having multiple versions of "the truth" about a business entity, both within individual applications, and distributed across applications.
Deluxe Paint
Deluxe Paint, often referred to as DPaint, is a bitmap graphics editor created by Dan Silva for Electronic Arts and published for the then-new Amiga 1000 in November 1985. A series of updated versions followed, some of which were ported to other platforms. An MS-DOS release with support for the 256 color VGA standard became popular for creating pixel graphics in video games in the 1990s. Author Dan Silva previously worked on the Cut & Paste word processor (1984), also from Electronic Arts. == History == Deluxe Paint began as an in-house art development tool called Prism. As author Dan Silva added features to Prism, it was developed as a showcase product to coincide with the Amiga's debut in 1985. Upon release, it was quickly embraced by the Amiga community and became the de facto graphics (and later animation) editor for the platform. Amiga manufacturer Commodore International later commissioned EA to create version 4.5 AGA to bundle with the new Advanced Graphics Architecture chipset (A1200, A4000) capable Amigas. Version 5 was the last release after Commodore's bankruptcy in 1994. Early versions of Deluxe Paint were available in protected and non copy-protected versions, the latter retailing for a slightly higher price. The copy protection scheme was later dropped. Deluxe Paint was first in a series of products from the Electronic Arts Tools group—then later moved to the ICE (for Interactivity, Creativity, and Education) group—which included such Amiga programs as Deluxe Music Construction Set (preceded by Music Construction Set for the Apple II), Deluxe Video, and the Studio series of paint programs for the Mac. With the development of Deluxe Paint, EA introduced the ILBM and ANIM file format standards for graphics. While widely used on the Amiga, these formats never gained widespread end user acceptance on other platforms, but were heavily used by game development companies. Deluxe Paint was used by LucasArts to make graphics for their adventure games such as The Secret of Monkey Island, and the name of a particular filename used to store the main protagonist Guybrush Threepwood was probably at the origin of his peculiar name. One of the main artist developer of the game, Mark Ferrari, in an interview for The Making of Monkey Island 30th Anniversary Documentary remembers that "there was a pulldown menu in DPaint called brushes, so character sprites were referred to as brushes", and the male protagonist was simply "the guy.brush" until the artist Steve Purcell suggested to take the very name "Guybrush". The author Ron Gilbert remembers that the PC DOS version of the file was named "guybrush.bbm". == Versions == === Amiga === Deluxe Paint I was released in 1985. A major feature was animation by using color cycling. The Amiga natively supports indexed color, where a pixel's color value does not carry any RGB hue information but instead is an index to a color palette (a collection of unique color values). By adjusting the color value in the palette, all pixels with that palette value change simultaneously in the image or animation, creating cyclic movement in the image. In the Christmas demo files on the Deluxe Paint I disk, this kind of animation (which is toggled by pressing the tab key) is used to depict falling snowflakes, a blinking Christmas tree, and a roaring fire in the fireplace. In 1986, Deluxe Paint II was introduced, which added many convenient features such as pattern and gradient fill, which could be selected by right-clicking on a fill tool. An effects menu with e.g. perspective transformation was also added. The screen format could now be changed from a dedicated selection page. Deluxe Paint III appeared in 1989 and added support for Extra Halfbrite. New editing modes allowed one to stencil certain colors to protect them, so it is possible to e.g. paint a landscape from front to back, with the foreground protected by a stencil. A major new feature of Deluxe Paint III was the ability to create cel-like animation, and animbrushes (1MB of RAM is needed for animation). These let the user pick up a section of an animation as an "animbrush", which can then be placed onto the canvas while it animates. Deluxe Paint III was one of the first paint programs to support animbrushes. This is similar to copy and paste, except one can pick up more than one image. Deluxe Paint IV (introduced in 1991), which did not include Silva as the lead programmer, offered significant new features like non-bitplane-indexed Hold-and-Modify support for creating images with up to 4,096 colors. Animation support was improved by adding a light table, i.e. onion skinning, and AnimBrush morphing. The color mixer was now a HAM region at the bottom of the screen (instead of a floating window as before) and allowed mixing adjacent colors similar to a real palette. Deluxe Paint 4.5 AGA appeared the following year, addressing the stability issues and providing support for the new A1200 and A4000 AGA machines and a revamped screen mode interface. It appeared in both standalone and Commodore-bundled versions. The final release, Deluxe Paint V, in 1995, supported true 24-bit RGB images. However, using only the AGA native chipset, the 24-bit RGB color was only held in computer memory, the on-screen image was displayed in HAM8 (18-bit color). === Apple IIGS === DeluxePaint II for the Apple IIGS was developed by Brent Iverson and released in 1987. === MS-DOS === Deluxe Paint II for MS-DOS was released in 1988, It required MS-DOS 2.0 and 640 kB of RAM. It supports CGA, EGA, MCGA, VGA, Hercules and Tandy IBM PC-compatible graphic cards. Deluxe Paint II Enhanced was released in 1989, requiring MS-DOS 2.11 and 640 kB of RAM. It supports resolutions up to 800x600 pixels with 256 colors. Deluxe Paint II Enhanced 2.0, released in 1994, was the most successful MS-DOS version, and was compatible with PC Paintbrush PCX image files. The MS-DOS conversion was done by Brent Iverson with the enhanced features by Steve Shaw. It supports CGA, EGA, MCGA, VGA, Hercules, Tandy, and Amstrad video cards, as well as early Super VGA video cards enabling it to support up to 800 × 600 with 256 (from 262,144) colors and 1024 × 768 with 16 colors. The sister product Deluxe Paint Animation (only for 320×200 pixels and 256 colors) was widely used, especially in video game development. === Atari ST === Deluxe Paint ST was developed by ArtisTech Development, published by Electronic Arts, and was released in 1990. It supports the Atari STE 4096 color palette and animated graphics. Features advertised for the Atari ST version include 3D perspective, design your own fonts, mirror symmetry, multi-color airbrushing & animations, printing up to poster size, split-screen magnification with variable zoom, and working on animations (including multiple animations). == Workflow == "[" and "]" hotkeys step through the indexed palette, turning indexed-pixel-painting into a fast two-handed mouse+keys process, and the right mouse button paints with the background color. For example, transparency is obtained as simply as selecting a background color index (a single right click on the palette GUI to change). colors could be locked from editing by use of a stencil (a list of color indices whose pixels should not be altered in the image data) and simple color-cycling animations could be created using contiguous entries in the palette. This was easy to change the hue and tone of a section of the image by altering the corresponding colors in the palette. (The specific section needed to use a dedicated part of the palette for this technique to work.) Brushes can be cut from the background by using the box, freehand, or polygon selection tools. They can then be used in the same manner as any other brush or pen. This functionality is simpler to use than the "stamp" tool of Photoshop or Alpha Channels as provided in later programs. Brushes can be rotated and scaled, even in 3D. After a brush is selected, it appears attached to the mouse cursor, providing an exact preview of what will be drawn. This allows precise pixel positioning of brushes. Animations stored in IFF ANIM format are delta compressed making animations both smaller and faster to playback. == Reception == Compute! criticized the documentation of the first release of DeluxePaint as inadequate, but stated that "DeluxePaint is a visual arts program of immense scope and flexibility". In later versions the documentation was much improved; for instance DeluxePaint IV came with a 300-page manual. Deluxe Paint was a hit for EA. The main line of the series, particularly installments one to three, has won a total of at least nine awards from independent publications and organizations, including three Amiga-specific awards. Deluxe Paint III also won Commodore International's Enterprise and Vision award in 1990, becoming the first software to win the award, for what the company's judges believed to be best utilizing the Amiga's graphical capabilities. Deluxe Pai
EdgeRank
EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed. As of 2011, Facebook has stopped using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account. EdgeRank was developed and implemented by Serkan Piantino. == Formula and factors == In 2010, a simplified version of the EdgeRank algorithm was presented as: ∑ e d g e s e u e w e d e {\displaystyle \sum _{\mathrm {edges\,} e}u_{e}w_{e}d_{e}} where: u e {\displaystyle u_{e}} is user affinity. w e {\displaystyle w_{e}} is how the content is weighted. d e {\displaystyle d_{e}} is a time-based decay parameter. User Affinity: The User Affinity part of the algorithm in Facebook's EdgeRank looks at the relationship and proximity of the user and the content (post/status update). Content Weight: What action was taken by the user on the content. Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts. Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public. A study has shown that it is possible to hypothesize a disadvantage of the "like" reaction and advantages of other interactions (e.g., the "haha" reaction or "comments") in content algorithmic ranking on Facebook. The "like" button can decrease the organic reach as a "brake effect of viral reach". The "haha" reaction, "comments" and the "love" reaction could achieve the highest increase in total organic reach. == Impact == EdgeRank and its successors have a broad impact on what users actually see out of what they ostensibly follow: for instance, the selection can produce a filter bubble (if users are exposed to updates which confirm their opinions etc.) or alter people's mood (if users are shown a disproportionate amount of positive or negative updates). As a result, for Facebook pages, the typical engagement rate is less than 1% (or less than 0.1% for the bigger ones), and organic reach 10% or less for most non-profits. As a consequence, for pages, it may be nearly impossible to reach any significant audience without paying to promote their content.